Screening of prognostic immune genes and establishment of prognostic model of cervical adenocarcinoma based on bioinformatics analysis.

BACKGROUND

There is currently a lack of clinical models to accurately predict the prognosis of cervical adenocarcinoma. This study aimed to explore the correlation between immune genes and the prognosis of cervical adenocarcinoma patients, and establish a prognostic model.

METHODS

Transcriptome sequencing data sets and clinical data sets of cervical adenocarcinoma samples were downloaded from the Gene Expression Omnibus (GEO) database. Information about the immune gene was obtained from the ImmPort database. Differentially expressed genes and differentially expressed immune genes were screened in cervical adenocarcinoma tissue and normal cervical group by edgeR package. Differentially expressed immune genes were screened for prognosis-related immune genes by Cox analysis. Taking the immune genes related to prognosis as variables, a prognosis prediction model was established by multivariate Cox regression analysis. Kaplan-Meier analysis and a receiver operating characteristic (ROC) curve were used to test the effectiveness of the model. According to the clinical information and risk score, univariate multivariate Cox analyses were used to screen the independent prognostic risk factors of cervical adenocarcinoma.

RESULTS

was an independent prognostic factor of cervical adenocarcinoma [hazard ratio (HR) =0.63; P=0.025]. (HR =1.22; P=0.034), (HR =1.33; P=0.023), (HR =1.53; P=0.024), and (HR =2.31; P=0.031) were prognostic risk factors for cervical adenocarcinoma. The risk score was calculated as follows: risk score = (0.005 × ) + (0.076 × ) + (0.061 × ) + (0.034 × ) + (-0.004 × ). The prognosis of the low-risk score group was better than that of the high-risk score group (P=0.035). The area under the ROC curve (AUC) of the risk score was 0.713, and the predictive power was good. Multivariate Cox analysis showed that N stage (HR =1.34; P=0.035) and risk score (HR =1.37; P<0.001) were independent risk factors for the prognosis of cervical adenocarcinoma (HR >1; P<0.001).

CONCLUSIONS

In this study, an immune gene prognosis prediction model for cervical adenocarcinoma was established based on the GEO and ImmPort databases. The prediction performance of the model is good, and the prognosis of patients can be evaluated according to the gene expression, which has clinical practicability.